2025
Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.
Miller R, Kavanagh P, Lemley M, Liang J, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Hayes S, Friedman J, Berman D, Dey D, Slomka P. Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2025, jnumed.124.268079. PMID: 39978815, DOI: 10.2967/jnumed.124.268079.Peer-Reviewed Original ResearchObstructive coronary artery diseaseCoronary artery diseaseArea under the receiver operating characteristic curveMyocardial perfusion imagingPerfusion scoreDetection of obstructive coronary artery diseaseDiagnostic accuracy of myocardial perfusion imagingPerfusion imagingAccuracy of myocardial perfusion imagingInvasive coronary angiographyCohort of patientsHighest area under the receiver operating characteristic curveLeft main coronary arteryReceiver operating characteristic curveStress TPDDeep learningObstructive CADMedian ageCoronary angiographyArtificial intelligenceDiagnostic performanceDiagnostic accuracyArtery diseaseAI predictionsCoronary arteryThe Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.
McCarty T, Shah R, Allencherril R, Moon N, Njei B. The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis. Journal Of Clinical Gastroenterology 2025 PMID: 39998988, DOI: 10.1097/mcg.0000000000002148.Peer-Reviewed Original ResearchConvolutional neural networkMalignant biliary stricturesAI-based systemsArtificial intelligence (AI)-based algorithmImage processing speedEndoscopic retrograde cholangiopancreatographyBiliary stricturesComputer visionPooled sensitivityNeural networkMachine learningDiagnostic performanceMeta-analysisIntelligent combinationIndeterminate biliary stricturesDiagnostic odds ratioImprove diagnostic yieldReceiver operating characteristic curveSystematic reviewOperating characteristics curveProcessing speedDiagnostic yieldRetrograde cholangiopancreatographyDiagnostic accuracyWorking group methodologyStandard to Handheld: A New Wave in Thoracic Ultrasound and Patient Care—A Direct Comparison of Portable Handheld Against Standard in Thoracic Ultrasound
Halim D, Kelly A, Hayes J, Bennett K, Tzouvelekis A, Ampazis D, Sampsonas F. Standard to Handheld: A New Wave in Thoracic Ultrasound and Patient Care—A Direct Comparison of Portable Handheld Against Standard in Thoracic Ultrasound. Medicina 2025, 61: 313. PMID: 40005430, PMCID: PMC11857366, DOI: 10.3390/medicina61020313.Peer-Reviewed Original ResearchConceptsLevels of experienceNon-inferiorityPortable ultrasound deviceOverall image qualityLung pathologyUltrasound deviceInterquartile rangeRespiratory departmentThoracic ultrasoundAnatomical visualizationHandheld ultrasound devicePatient careImprove healthcareOperator's level of experienceNo significant differenceLikert scaleTrainee levelStatistically significant variablesDiagnostic accuracyClinical decisionsOverall perceptionOnline questionnaireClinical utilityMedian rateClinical settingCD177, MYBL2, and RRM2 Are Potential Biomarkers for Musculoskeletal Infections.
Agidigbi T, Fram B, Molloy I, Riedel M, Wiznia D, Oh I. CD177, MYBL2, and RRM2 Are Potential Biomarkers for Musculoskeletal Infections. Clinical Orthopaedics And Related Research® 2025 PMID: 39915095, DOI: 10.1097/corr.0000000000003402.Peer-Reviewed Original ResearchPeripheral blood mononuclear cellsFracture-related infectionRibonucleotide reductase regulatory subunit M2Peripheral blood mononuclear cell levelsPeripheral blood mononuclear cell concentrationsPeriprosthetic joint infectionMusculoskeletal infectionsMonitoring treatment responseDiabetic foot infectionsTreatment responseReceiver operating characteristicPurulent drainageControl groupSerum levelsSinus tractInflammatory markersBlood samplesFoot infectionsInfection groupDiagnostic accuracyTranscriptomic analysis of peripheral blood mononuclear cellsDiagnosis of musculoskeletal infectionsNormalization of inflammatory markersPeripheral blood mononuclear cells of patientsInflammatory conditionsDiagnosis of cancer therapy-related cardiovascular toxicities: A multimodality integrative approach and future developments
Travers S, Alexandre J, Baldassarre L, Salem J, Mirabel M. Diagnosis of cancer therapy-related cardiovascular toxicities: A multimodality integrative approach and future developments. Archives Of Cardiovascular Diseases 2025, 118: 185-198. PMID: 39947997, DOI: 10.1016/j.acvd.2024.12.012.Peer-Reviewed Original ResearchConceptsCardio-oncologyCardiovascular toxicityCardiovascular imagingMultimodality cardiovascular imagingCancer risk factorsIncrease diagnostic accuracyPrognostic stratificationNatriuretic peptideSerum biomarkersTherapy schemesDiagnostic accuracyCancer therapyRhythm disordersRisk factorsCardiovascular diseaseBiomarkersMultimodal integrated approachCancerOmics approachesToxicityRhythmTherapyTroponinSerumDiagnosisPhenotypic and genotypic characterization of Mycobacterium tuberculosis pyrazinamide resistance—India, 2018–2020
Tamilzhalagan S, Justin E, Selvaraj A, Venkateswaran K, Sivakumar A, Chittibabu S, McLaughlin H, Moonan P, Smith J, Suba S, Narayanan M, Ho C, Kumar N, Tripathy S, Shanmugam S, Hall-Eidson P, Ranganathan U. Phenotypic and genotypic characterization of Mycobacterium tuberculosis pyrazinamide resistance—India, 2018–2020. Frontiers In Microbiology 2025, 15: 1515627. PMID: 39845030, PMCID: PMC11750862, DOI: 10.3389/fmicb.2024.1515627.Peer-Reviewed Original ResearchPZA resistancePyrazinamide resistanceMultidrug resistanceDuration of tuberculosis treatmentWhole-genome sequencingPrevalence of mutationsSecond-line drugsPZA-resistant isolatesResistance-conferring mutationsGenome sequenceTB burden countriesLineage 2Genotypic characterizationResistance markersNovel mutationsPhenotypic resistanceMutational diversityDiagnostic accuracyTuberculosis treatmentAntituberculosis drugsCo-resistanceBurden countriesPyrazinamideMutationsTB preventionAutopsy: Infectious and Serious Communicable Diseases
Gill J, Brooks E. Autopsy: Infectious and Serious Communicable Diseases. 2025, 455-467. DOI: 10.1016/b978-0-443-21441-7.00021-2.Peer-Reviewed Original ResearchHuman immunodeficiency virusViral hepatitis BRisk of occupational infectionOccupational infectionInfection control practicesForensic autopsy populationImmunodeficiency virusHepatitis BInfectious disease testingTuberculosis infectionInfectious disease test resultsDiagnostic accuracyPostexposure managementInfectionSARS-CoV-2Autopsy populationAutopsyMicrobiological testsEbola virusPostmortem examinationDisease testingInfectious diseasesLaboratory personnelPreventive measuresDeath
2024
Beyond cholangiocarcinoma: imaging features of mimicking pathologies in the biliary tract
Khasawneh H, O’Brien C, Czeyda-Pommersheim F, Qayyum A, Miller F, Arif Tiwari H, Paspulati R, Kierans A. Beyond cholangiocarcinoma: imaging features of mimicking pathologies in the biliary tract. Abdominal Radiology 2024, 1-16. PMID: 39710762, DOI: 10.1007/s00261-024-04749-z.Peer-Reviewed Original ResearchMalignant conditionsOverlap of imaging findingsDiagnosis of cholangiocarcinomaPrimary malignancyDifferentiate cholangiocarcinomaHistopathological confirmationImaging findingsMorphological subtypesDifferential diagnosisBiliary tractHeterogeneous diseaseDiagnostic accuracyPatient managementCholangiocarcinomaAccurate diagnosisHepatobiliary systemImage featuresPeriductal-infiltratingClinical practiceImaging characteristicsDiagnosisDiagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis
Sharifi G, Hajibeygi R, Zamani S, Easa A, Bahrami A, Eshraghi R, Moafi M, Ebrahimi M, Fathi M, Mirjafari A, Chan J, Dixe de Oliveira Santo I, Anar M, Rezaei O, Tu L. Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis. Emergency Radiology 2024, 32: 97-111. PMID: 39680295, DOI: 10.1007/s10140-024-02300-7.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArea under the receiver operating characteristic curveConvolutional neural network modelCT scanSkull fractureComputed tomographyDeep learningProspective clinical trialMeta-analysisReceiver operating characteristic curvePublication biasSkull fracture detectionSystematic reviewNeural network algorithmDetecting skull fracturesImprove diagnosis accuracyDiagnostic hurdlesShortage of radiologistsAutomated diagnostic toolTransfer learningDiagnostic performanceDiagnostic accuracyClinical trialsModel architectureNeural networkDiastolic dysfunction evaluation by cardiovascular magnetic resonance derived E, a, e’: Comparison to echocardiography
Lamy J, Xiang J, Shah N, Kwan J, Kim Y, Upadhyaya K, Reinhardt S, Meadows J, McNamara R, Baldassarre L, Peters D. Diastolic dysfunction evaluation by cardiovascular magnetic resonance derived E, a, e’: Comparison to echocardiography. Physiological Reports 2024, 12: e70078. PMID: 39604208, PMCID: PMC11602526, DOI: 10.14814/phy2.70078.Peer-Reviewed Original ResearchConceptsCardiovascular magnetic resonanceTransthoracic echocardiographyDiastolic dysfunctionDiastolic functionDiagnostic accuracy of cardiovascular magnetic resonanceEvaluate diastolic dysfunctionCardiovascular magnetic resonance imagingLeft atrial volumeMitral annular velocityHealthy age-matched subjectsComparison to echocardiographyMitral inflow velocityEvaluate diastolic functionAge-matched subjectsPresence of DDAtrial volumeDD gradeFirst-lineAnnular velocityDiagnostic accuracyImaging modalitiesMagnetic resonanceEchocardiographyALLTransthoracicIncorporation of the central vein sign into the McDonald criteria
Amin M, Nakamura K, Daboul L, O'Donnell C, Cao Q, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi P, Cree B, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Pelletier D, Prčkovska V, Raza P, Ramos M, Samudralwar R, Schindler M, Sotirchos E, Sicotte N, Solomon A, Shinohara R, Reich D, Sati P, Ontaneda D. Incorporation of the central vein sign into the McDonald criteria. Multiple Sclerosis And Related Disorders 2024, 93: 106182. PMID: 39622133, PMCID: PMC11779579, DOI: 10.1016/j.msard.2024.106182.Peer-Reviewed Original ResearchCentral vein signDIS criteriaDiagnostic performanceMultiple sclerosisDeep white matter lesionsDiagnosis of multiple sclerosisMulti-center studyInternational multi-center studyWhite matter lesionsNorth American ImagingMcDonald CriteriaProspective studyDiagnostic accuracyMRI disseminationDemyelinating diseaseBackground DiagnosisMS diagnosisDiagnostic biomarkersCompare sensitivityLesionsBrain locationsMethods DataBrain imagingBrainSignsConstruction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score
Moore C, Gross C, Hart L, Molinaro A, Rhodes D, Singh D, Baloescu C. Construction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score. Journal Of The American College Of Emergency Physicians Open 2024, 5: e13324. PMID: 39524039, PMCID: PMC11543628, DOI: 10.1002/emp2.13324.Peer-Reviewed Original ResearchClinical prediction ruleArea under the receiver operating characteristic curveSTONE scoreMultivariate logistic regressionUreteral stonesComputed tomographyPrediction ruleUncomplicated renal colicKidney stonesReceiver operating characteristic curveLogistic regressionNon-black raceDiagnosis of kidney stonesGross hematuriaMicroscopic hematuriaRenal colicPotential adverse effectsDiagnostic accuracyHematuriaClinical algorithmMale genderProspective dataClinical accuracyRetrospective dataCharacteristic curveInter-reader reliability and diagnostic accuracy of PI-RADS scoring between academic and community care networks: How wide is the gap?
Smani S, Jalfon M, Sundaresan V, Lokeshwar S, Nguyen J, Halstuch D, Khajir G, Cavallo J, Sprenkle P, Leapman M, Kim I. Inter-reader reliability and diagnostic accuracy of PI-RADS scoring between academic and community care networks: How wide is the gap? Urologic Oncology Seminars And Original Investigations 2024 PMID: 39438211, DOI: 10.1016/j.urolonc.2024.10.002.Peer-Reviewed Original ResearchClinically significant PCaPI-RADS scoreDetect clinically significant PCaReporting of prostate cancerInter-reader reliabilityProstate cancerPI-RADSDiagnostic accuracyReceiver operating characteristicInter-readerPresence of clinically significant PCaProstate Imaging Reporting & Data SystemDetect clinically significant diseasePI-RADS 3 lesionsTertiary academic care centerMRI fusion biopsyPI-RADS 3Significant PCaProstate cancer managementClinically significant diseaseInter-reader agreementSubgroup of lesionsAcademic care centerIntraclass correlationMpMRI interpretationDirect and rapid detection of serum amino acid and monoamine neurotransmitters to assist the diagnosis of panic disorder
Liu S, Wan X, Zhao M, Wang J, Wu W, You L, Yuan Y, Xu Q, Gao R. Direct and rapid detection of serum amino acid and monoamine neurotransmitters to assist the diagnosis of panic disorder. Science China Chemistry 2024, 68: 377-384. DOI: 10.1007/s11426-024-2150-5.Peer-Reviewed Original ResearchPanic disorderDART-MSMonoamine neurotransmittersClinical diagnosis of PDAnalysis of mass spectrometryMagnetic solid phase extraction methodSerum amino acidsSolid phase extraction methodDiagnosis of panic disorderSerum of PD patientsSolid phase extractionMetabolic abnormalitiesPhase extraction methodDiagnostic accuracyKyn pathwayDiagnosis of PDClinical diagnosisPD patientsPhase extractionNeurotransmitterMass spectrometryNeurotransmitter detectionMagnetic Fe3O4Measurement of amino acidsBiological samplesEarly Disease-Modifying Treatments for Presymptomatic Multiple Sclerosis
Zeydan B, Azevedo C, Makhani N, Cohen M, Tutuncu M, Thouvenot E, Siva A, Okuda D, Kantarci O, Lebrun-Frenay C. Early Disease-Modifying Treatments for Presymptomatic Multiple Sclerosis. CNS Drugs 2024, 38: 973-983. PMID: 39285136, PMCID: PMC11560559, DOI: 10.1007/s40263-024-01117-9.Peer-Reviewed Original ResearchRadiologically isolated syndromeRisk factorsLaboratory biomarkersMultiple sclerosisDisease-modifying treatmentsSpinal cordGadolinium-enhancing lesionsEfficacy of disease-modifying treatmentsRandomized clinical trialsIncrease diagnostic accuracyLack of clinical guidelinesSymptomatic MSNeurofilament-light chainCSF abnormalitiesPresymptomatic individualsAdverse eventsMale sexMS criteriaClinical eventsDiagnostic accuracyClinical trialsDisease outcomeClinical guidelinesYounger ageLesionsExhaled breath analysis: A promising triage test for tuberculosis in young children
Bijker E, Smith J, Mchembere W, McCarthy K, Oord H, Gerritsen J, Click E, Cain K, Song R. Exhaled breath analysis: A promising triage test for tuberculosis in young children. Tuberculosis 2024, 149: 102566. PMID: 39332067, PMCID: PMC11864270, DOI: 10.1016/j.tube.2024.102566.Peer-Reviewed Original ResearchTriage testPaediatric pulmonary tuberculosisRespiratory tract infectionsSymptoms of tuberculosisDiagnose respiratory tract infectionsCross-sectional studyExpectorated sputumTract infectionsPulmonary tuberculosisPaediatric tuberculosisDiagnostic accuracyBreath testYoung childrenYoung infantsTuberculosisExhaled breath analysisBreathingBreath analysisChildrenTriageExhaled breathSputumInfantsMisdiagnosis of Multiple Sclerosis: Past, Present, and Future
Rjeily N, Solomon A. Misdiagnosis of Multiple Sclerosis: Past, Present, and Future. Current Neurology And Neuroscience Reports 2024, 24: 547-557. PMID: 39243340, DOI: 10.1007/s11910-024-01371-w.Peer-Reviewed Original ResearchMS diagnostic criteriaMultiple sclerosisDiagnostic criteriaCentral vein signParamagnetic rim lesionsAssociated with misdiagnosisRecent FindingsRecent studiesMS misdiagnosisMRI findingsEvaluating patientsSuspected MSDiagnostic accuracyRim lesionsFindingsRecent studiesImaging biomarkersMisdiagnosisDiagnostic biomarkersCommon disordersPatientsBiomarkersPotential strategyDiagnostic performance of central vein sign versus oligoclonal bands for multiple sclerosis
Toljan K, Daboul L, Raza P, Martin M, Cao Q, O’Donnell C, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi P, Cree B, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Pelletier D, Samudralwar R, Schindler M, Sotirchos E, Sicotte N, Solomon A, Shinohara R, Reich D, Sati P, Ontaneda D. Diagnostic performance of central vein sign versus oligoclonal bands for multiple sclerosis. Multiple Sclerosis Journal 2024, 30: 1268-1277. PMID: 39234802, PMCID: PMC11421977, DOI: 10.1177/13524585241271988.Peer-Reviewed Original ResearchConceptsCentral vein signPositive predictive valueOligoclonal bandsDiagnostic performanceMS diagnosisCerebrospinal fluidMultiple sclerosisPredictive valueNegative predictive valueCerebrospinal fluid testingRadiological suspicionDiagnostic accuracyImaging biomarkersDiagnosisDiagnostic biomarkersMonthsSclerosisBiomarkersPilot studyBaselineSelection 3The Diagnostic Accuracy of SPECT Imaging in Patients With Suspected Pulmonary Embolism
Bang J, Lee W, Cho S, Choi M, Song Y. The Diagnostic Accuracy of SPECT Imaging in Patients With Suspected Pulmonary Embolism. Clinical Nuclear Medicine 2024, 49: 637-643. PMID: 38831512, DOI: 10.1097/rlu.0000000000005167.Peer-Reviewed Original ResearchConceptsChronic thromboembolic pulmonary hypertensionThromboembolic pulmonary hypertensionPulmonary embolismV/Q SPECT/CTDiagnostic accuracyPulmonary hypertensionQ-SPECT/CTSPECT imaging modalitiesDiagnostic accuracy of SPECT imagingSuspected chronic thromboembolic pulmonary hypertensionSuspected acute pulmonary embolismSPECT imagesPlanar imagingSpecificity of SPECTSuspected acute PEImaging modalitiesSuspected pulmonary embolismAcute pulmonary embolismAccuracy of SPECTAccuracy of SPECT imagesMeta-analysesSearch of MEDLINEDiagnostic accuracy of SPECTQ-SPECTNetwork meta-analysesArtificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases
Retamero J, Gulturk E, Bozkurt A, Liu S, Gorgan M, Moral L, Horton M, Parke A, Malfroid K, Sue J, Rothrock B, Oakley G, DeMuth G, Millar E, Fuchs T, Klimstra D. Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases. The American Journal Of Surgical Pathology 2024, 48: 846-854. PMID: 38809272, PMCID: PMC11191045, DOI: 10.1097/pas.0000000000002248.Peer-Reviewed Original ResearchConceptsLymph node metastasisNode metastasisDetection of lymph node metastasesBreast cancer lymph node metastasisMetastasis detection rateWhole slide imagesBreast cancer stageArtificial intelligenceIncrease diagnostic accuracyReading pathologistsCancer stageDiagnostic accuracyMetastasisCancer metastasisAverage reading timeBreastPathologistsDetection rateAI algorithms
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